Online Course Developer –
Time Series Forecasting and Operational Analytics
Location:
Remote (U.S.-based only)
Division:
Rabb School of Continuing Studies, Brandeis University
Compensation:
$3,000.00 (Approx. 65 hours over 12 weeks)
Brandeis University’s Rabb School of Continuing Studies is seeking a skilled online course developer to design and build a new three credit asynchronous online course titled: Time Series Forecasting and Operational Analytics
.
This role is for an experienced academic and curriculum strategist to serve as an Online Course Developer within Brandeis Online’s graduate program. The developer will design and build asynchronous, instructor-facilitated online courses aligned with institutional learning outcomes, accreditation standards, and workforce relevance This course will cover predictive modeling and forecasting under uncertainty, including ARIMA, Prophet, and deep learning approaches for sustainable operations.
Responsibilities:
The development of an online asynchronous course entails the creation and/or selection of elements as outlined in the Brandeis Online Course Standards. Required components include a Brandeis-compliant syllabus, instructor-created materials informed by current industry knowledge, learning objects, and applied assignments and assessments aligned to course and program outcomes.
The Developer is responsible for the substantive content and pedagogical strategies of the course and agrees to uphold Brandeis’s academic standards and online course development guidelines.
Throughout the design process, the Developer will collaborate with Brandeis Online staff, adhere to technical requirements for LMS integration, and meet project milestones. Course drafts will be submitted at designated intervals for feedback, and final approval will be contingent upon a comprehensive design review by a Learning Designer, and Brandeis Online.
Qualifications:
Advanced degree (Masters or Ph.D) in Statistics, Operational Research, Data Science or a related field.
Professional experience applying forecasting methods to operational demands, planning, or in sustainability contexts.
Expertise in time series analysis and forecasting under uncertainty, including ARMIA and modern machine learning approach.
At least year of teaching or training experience (preferably online/asynchronous).
Minimum 1 year experience developing asynchronous online courses for adult learners in higher education.
Proficiency with LMS platforms and digital authoring tools.
Familiarity with analytical tools, collaborative platforms, and interdisciplinary teamwork.
Strong communication, organization, and independent work skills.
Familiarity with curriculum design, accreditation standards, and graduate-level rigor.
Ability to translate interdisciplinary content into engaging, accessible learning pathways.
Strong writing and editing skills to produce cohesive, learner-centered experiences.
Preferred Experience:
Experience teaching or developing graduate-level online courses.
Knowledge of global learner personas and culturally responsive pedagogy.
Familiarity with Moodle LMS and digital authoring tools (e.g., H5P).
Familiarity with experiential learning models and employer-aligned curriculum.
Additional Details:
Fully remote (U.S.-based applicants only; no visa sponsorship)
12-week development timeline (~65 total hours)
Compensation: $3,000.00
Pay Range Disclosure
The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.
Equal Opportunity Statement
Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").